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首页> 外文期刊>Cereal Chemistry >Development and Validation of Prediction Models for Rice Surface Lipid Content and Color Parameters Using Near-Infrared Spectroscopy: A Basis for Predicting Rice Degree of Milling.
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Development and Validation of Prediction Models for Rice Surface Lipid Content and Color Parameters Using Near-Infrared Spectroscopy: A Basis for Predicting Rice Degree of Milling.

机译：利用近红外光谱技术开发和验证水稻表面脂质含量和颜色参数的预测模型：预测碾米程度的基础。

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摘要

Degree of milling (DOM) of rice plays a key role in determining rice quality and value. Therefore, accurate, nondestructive, quick, and automated surface lipid content (SLC) measurement would be useful in a commercial milling environment. This study was undertaken to provide calibration models for commercial use to provide quick and accurate evaluation of milled rice SLC and Hunterlab color parameters (L,a,b) as indications of rice DOM. In all, 960 samples, including seven cultivars from seven southern United States locations, stored for 0, 1, 2, 3, and 6 months, were milled for four durations to obtain samples of varying DOM. The samples were used to develop calibration models of milled rice SLC and L,a,b values. Another sample set (n = 58) was commercially milled and used to validate the developed models. A DA 7200 diode array analyzer was used to scan milled rice samples in wavelength spectra of 950-1,650 nm. SLC and color parameters were measured using a Soxtec system and a HunterLab colorimeter, respectively. The partial least squares regression (PLS) method using the full near-infrared spectra was used to develop prediction models for rice SLC and color parameters. Milled rice SLC was well fitted with a correlation of determination of predicted and measured values of (R2 = 0.934). Color parameters were also successfully fitted for L (R2 = 0.943), a (R2 = 0.870), and b (R2 = 0.855). Performance of the developed models to predict rice DOM was superior in predicting SLC and L,a,b values with R2 predicted and measured values of 0.958, 0.836, 0.924, and 0.661, respectively.
机译：稻米的碾磨度（DOM）在确定稻米质量和价值方面起着关键作用。因此，准确的，无损的，快速的和自动的表面脂质含量（SLC）测量在商业研磨环境中将是有用的。进行这项研究以提供用于商业用途的校准模型，以提供对碾米SLC和Hunterlab颜色参数（L，a，b）的快速和准确评估，以此作为稻米DOM的指标。总共将960个样品（包括来自美国南部七个地点的七个品种）分别存储了0、1、2、3和6个月，进行了四个时间的研磨，以获取不同DOM的样品。样品用于建立碾米SLC和L，a，b值的校准模型。将另一组样品（n = 58）进行商业研磨，并用于验证开发的模型。使用DA 7200二极管阵列分析仪扫描950-1,650 nm波长光谱中的碾米样品。 SLC和颜色参数分别使用Soxtec系统和HunterLab比色计进行测量。使用全近红外光谱的偏最小二乘回归（PLS）方法开发了水稻SLC和颜色参数的预测模型。碾米SLC与预测值和测量值（R2 = 0.934）的确定值非常相关。颜色参数也已成功拟合为L（R2 = 0.943），a（R2 = 0.870）和b（R2 = 0.855）。所开发的模型预测稻米DOM的性能在预测SLC和L，a，b值方面表现优异，而R2预测值和实测值分别为0.958、0.836、0.924和0.661。

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